Deep neural networks based wrist print region segmentation and classification
Abstract
In recent years, biometric recognition based systems have become widespread. One of these is wrist-based recognition systems. In this study, wrist print based recognition system was developed by using near infrared (NIR) camera. Totally 220 NIR camera images taken from 10 for each both hands of 11 people. The obtained data set is allocated 70% (154 images) for training and 30% (66 images) for testing. The wrist regions are labeled on the training set images. Data sets were created with two different labeling methods. In the first data set, only the wrist regions were labeled and it was aimed to segment the wrist region from the image. In the second data set, the wrist images were labeled according to 22 classes and these classes were tried to be predicted. The labeled data was trained with YOLOV2 architecture supported by ResNet50 one of the deep neural network models. The trained model was tested with the remaining 30% of the data set. In the test process, the wrist region was determined in the NIR images with the trained model. As a results of the study, it was seen that the wrist regions were correctly detected in all first data set test images and the mean value of obtained similarity rates was 95.26%. In the test results of the second dataset, 92.43% classification success was obtained. Therefore, it can be said that the deep learning architectures ResNet and YOLO are effective in the segmentation of the wrist region.
Keywords
wrist print recognition, deep neural networks, near-infrared camera, YOLO
Supporting Institution
ICENTE 2020
Project Number
ICENTE20-0105
Thanks
Dear Kerim Kursat Cevik ,
The paper with the id and title ICENTE20-0105 : WRIST PRINT REGION SEGMENTATION BASED ONDEEP NEURAL NETWORKS, that you sent to the ICENTE20 conference has been selected for publication inMANAS Journal of Engineering (MJEN) journal.
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References
- [1] Wikipedia. "Residual neural network." Wikipedia. https://en.wikipedia.org/wiki/Residual_neural_network (accessed 15.11.2020, 2020).
- [2] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
- [3] A. Amidi, S. Amidi, D. Vlachakis, V. Megalooikonomou, N. Paragios, and E. I. Zacharaki, "EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation," PeerJ, vol. 6, p. e4750, 2018.
- [4] S. Amidi, A. Amidi, D. Vlachakis, N. Paragios, and E. I. Zacharaki, "Automatic single-and multi-label enzymatic function prediction by machine learning," PeerJ, vol. 5, p. e3095, 2017.
- [5] B. Kuyumcu, B. Buluz, and Y. Kömeçoğlu, "Author Identification in Turkish Documents with Ridge Regression Analysis," in 2019 27th Signal Processing and Communications Applications Conference (SIU), 2019: IEEE, pp. 1-4.
- [6] J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263-7271.
- [7] C. C. Nguyen et al., "Towards real-time smile detection based on faster region convolutional neural network," in 2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR), 2018: IEEE, pp. 1-6.
- [8] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779-788.
- [9] L. Wang, G. Leedham, and S.-Y. Cho, "Infrared imaging of hand vein patterns for biometric purposes," IET computer vision, vol. 1, no. 3, pp. 113-122, 2007.
- [10] L. Hong, Y. Wan, and A. Jain, "Fingerprint image enhancement: algorithm and performance evaluation," IEEE transactions on pattern analysis and machine intelligence, vol. 20, no. 8, pp. 777-789, 1998.